What is the vanishing gradient problem and how do transformers avoid it?
This tests your understanding of core deep learning training issues and the transformer's specific architectural solutions. A great answer defines vanishing gradients in sequential models, then explains how the transformer's parallel attention mechanism creates direct, short paths for gradients between any two tokens, regardless of distance. A red flag is vaguely mentioning 'attention' without explaining why its parallel nature is the key to solving the problem for long sequences.
This question probes your fundamental understanding of why deep sequential models like RNNs struggle with long-range dependencies and how the transformer's architecture solves this. It's a test of first principles, not just component memorization. A strong answer first defines the vanishing gradient problem: the exponential shrinking of gradients during backpropagation, which prevents learning across long sequences. Then, explain that transformers use a parallel multi-head attention mechanism, creating direct O(1) paths between all tokens. This short, non-sequential path for gradient flow prevents the signal from decaying, unlike in an RNN.
Read the original → Wikipedia: Transformer (deep learning architecture)
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